Data analysts oftentimes desire to transform a set of data values to a particular data format (also sometimes referred to as data wrangling). For example, data may be collected in various formats or variations. To effectively analyze or consume the data, however, the collected data is desired to be transformed to a standard or common format. Upon transforming the diverse data to a consistent format, such transformed data can be analyzed (e.g., to collect statistics). Example data transformations include, for example, date-time conversions, address parsing, name conversions, etc.
Performing data transformations, however, is often difficult and time consuming. For example, because data might be collected in numerous, diverse formats, a different transformation may be applied to each of the different format types and, as such, result in an extensive amount of time to perform each of the transformations. In particular, a user may be required to manually search for, or develop, a data transformation operation or set of data transformation operations to apply to a collected data set in order to accomplish a uniform set of data values. In order to more efficiently perform data transformations, it is important that a user be able to effectively search for and/or utilize transformation operations that transform data as desired by the user.